1. Field
This disclosure relates generally to frequency control, and more particularly to control of electronic oscillator output.
2. Description of Related Art
Neural networks can be found abundantly in nature. The brains of all animals including humans comprise vast neural networks. Biological neural networks comprise biological neurons that are chemically connected or functionally related in the peripheral nervous system or the central nervous system of certain living creatures. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network, called synapses, may be extensive. Apart from electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex. As we know from the human condition, neural networks are extremely efficient at learning and adapting to changing environments, and more proficient than computers at certain difficult tasks such as image recognition. As an example of the utilization of neural networks in nature, bats fly at high speed and use their neural network to process their ultrasonic emissions to map their environment, calculate pursuit algorithms, and find and capture prey without injuring themselves, among other things.
Artificial Neural Networks (ANN's), also referred to herein as neural networks (NN's), are mankind's attempt at replicating nature's elegant solution. An ANN is a mathematical model or computational model that is modeled after, or inspired by, the structure and functionality of biological neural networks. To date, currently implemented ANN's have not provided the efficiency or computational power of the human brain.